Analysis of DNA variations in genes under selection on the mountain pine beetle (MPB) fungal associates
Bibliographic record
Abstract
The Mountain Pine Beetle (MPB), Dendroctonus ponderosa Hopkins, is a small bark beetle that has affected over 13 million hectares of forests in western Canada and the USA since 1990, and more recently has affected forests at higher elevations and more northern latitudes (Logan et al. 2001). The MPB attacks the trees alone; it has a mutualistic association with fungal species that are mainly in the Ophiostomoid family of the Ascomycota phylum, which provides many biological benefits (Lee et al. 2006a). To develop a better understanding of the biology and to provide information for the development of MPB growth models, single-nucleotide polymorphisms (SNPs) were identified from multiple candidate Grosmannia clavigera DNA samples. G. clavigera is one of the mutualistic fungal species, and DNA samples were used from various populations in western North America. PCR using DNA oligonucleotides was the main method to obtain the amplified genes for sequencing. After sequencing at Laval University, Geneious software was used to contig, edit, and align sequences. Using concatenated sequences, SNPs that were found within the adaptive genes were used to construct three different phylogenetic diagrams. The geographical locations of the populations tested in the phylogenetic analysis were compared to the genetic distance of the different samples. The data shows that there is a trend where populations of G. clavigera that are further apart geographically have a greater genetic distance, and populations that are geographically closer together share similar SNP variations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".